class_docstr: Computes internal influence by approximating the integral of gradients
  for a particular layer along the path from a baseline input to the
  given input.
  If no baseline is provided, the default baseline is the zero tensor.
  More details on this approach can be found here:
  https://arxiv.org/abs/1802.03788
  
  Note that this method is similar to applying integrated gradients and
  taking the layer as input, integrating the gradient of the layer with
  respect to the output.
class_docstr_tok: ["Computes","internal","influence","by","approximating","the","integral","of","gradients","for","a","particular","layer","along","the","path","from","a","baseline","input","to","the","given","input",".","If","no","baseline","is","provided",",","the","default","baseline","is","the","zero","tensor",".","More","details","on","this","approach","can","be","found","here",":","https",":","\/\/arxiv.org\/abs\/1802.03788","Note","that","this","method","is","similar","to","applying","integrated","gradients","and","taking","the","layer","as","input",",","integrating","the","gradient","of","the","layer","with","respect","to","the","output","."]
docstr: Args:
  
      inputs (Tensor or tuple[Tensor, ...]): Input for which internal
                  influence is computed. If forward_func takes a single
                  tensor as input, a single input tensor should be provided.
                  If forward_func takes multiple tensors as input, a tuple
                  of the input tensors should be provided. It is assumed
                  that for all given input tensors, dimension 0 corresponds
                  to the number of examples, and if multiple input tensors
                  are provided, the examples must be aligned appropriately.
      baselines (scalar, Tensor, tuple of scalar, or Tensor, optional):
                  Baselines define a starting point from which integral
                  is computed and can be provided as:
  
                  - a single tensor, if inputs is a single tensor, with
                    exactly the same dimensions as inputs or the first
                    dimension is one and the remaining dimensions match
                    with inputs.
  
                  - a single scalar, if inputs is a single tensor, which will
                    be broadcasted for each input value in input tensor.
  
                  - a tuple of tensors or scalars, the baseline corresponding
                    to each tensor in the inputs' tuple can be:
  
                    - either a tensor with matching dimensions to
                      corresponding tensor in the inputs' tuple
                      or the first dimension is one and the remaining
                      dimensions match with the corresponding
                      input tensor.
  
                    - or a scalar, corresponding to a tensor in the
                      inputs' tuple. This scalar value is broadcasted
                      for corresponding input tensor.
  
                  In the cases when `baselines` is not provided, we internally
                  use zero scalar corresponding to each input tensor.
  
                  Default: None
      target (int, tuple, Tensor, or list, optional): Output indices for
                  which gradients are computed (for classification cases,
                  this is usually the target class).
                  If the network returns a scalar value per example,
                  no target index is necessary.
                  For general 2D outputs, targets can be either:
  
                  - a single integer or a tensor containing a single
                    integer, which is applied to all input examples
  
                  - a list of integers or a 1D tensor, with length matching
                    the number of examples in inputs (dim 0). Each integer
                    is applied as the target for the corresponding example.
  
                  For outputs with > 2 dimensions, targets can be either:
  
                  - A single tuple, which contains #output_dims - 1
                    elements. This target index is applied to all examples.
  
                  - A list of tuples with length equal to the number of
                    examples in inputs (dim 0), and each tuple containing
                    #output_dims - 1 elements. Each tuple is applied as the
                    target for the corresponding example.
  
                  Default: None
      additional_forward_args (Any, optional): If the forward function
                  requires additional arguments other than the inputs for
                  which attributions should not be computed, this argument
                  can be provided. It must be either a single additional
                  argument of a Tensor or arbitrary (non-tuple) type or a
                  tuple containing multiple additional arguments including
                  tensors or any arbitrary python types. These arguments
                  are provided to forward_func in order following the
                  arguments in inputs.
                  For a tensor, the first dimension of the tensor must
                  correspond to the number of examples. It will be
                  repeated for each of `n_steps` along the integrated
                  path. For all other types, the given argument is used
                  for all forward evaluations.
                  Note that attributions are not computed with respect
                  to these arguments.
                  Default: None
      n_steps (int, optional): The number of steps used by the approximation
                  method. Default: 50.
      method (str, optional): Method for approximating the integral,
                  one of `riemann_right`, `riemann_left`, `riemann_middle`,
                  `riemann_trapezoid` or `gausslegendre`.
                  Default: `gausslegendre` if no method is provided.
      internal_batch_size (int, optional): Divides total #steps * #examples
                  data points into chunks of size at most internal_batch_size,
                  which are computed (forward / backward passes)
                  sequentially. internal_batch_size must be at least equal to
                  #examples.
                  For DataParallel models, each batch is split among the
                  available devices, so evaluations on each available
                  device contain internal_batch_size / num_devices examples.
                  If internal_batch_size is None, then all evaluations
                  are processed in one batch.
                  Default: None
      attribute_to_layer_input (bool, optional): Indicates whether to
                  compute the attribution with respect to the layer input
                  or output. If `attribute_to_layer_input` is set to True
                  then the attributions will be computed with respect to
                  layer inputs, otherwise it will be computed with respect
                  to layer outputs.
                  Note that currently it is assumed that either the input
                  or the output of internal layer, depending on whether we
                  attribute to the input or output, is a single tensor.
                  Support for multiple tensors will be added later.
                  Default: False
  
  Returns:
      *Tensor* or *tuple[Tensor, ...]* of **attributions**:
        - **attributions** (*Tensor* or *tuple[Tensor, ...]*):
                  Internal influence of each neuron in given
                  layer output. Attributions will always be the same size
                  as the output or input of the given layer depending on
                  whether `attribute_to_layer_input` is set to `False` or
                  `True` respectively.
                  Attributions are returned in a tuple if
                  the layer inputs / outputs contain multiple tensors,
                  otherwise a single tensor is returned.
  
  Examples::
  
      >>> # ImageClassifier takes a single input tensor of images Nx3x32x32,
      >>> # and returns an Nx10 tensor of class probabilities.
      >>> # It contains an attribute conv1, which is an instance of nn.conv2d,
      >>> # and the output of this layer has dimensions Nx12x32x32.
      >>> net = ImageClassifier()
      >>> layer_int_inf = InternalInfluence(net, net.conv1)
      >>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
      >>> # Computes layer internal influence.
      >>> # attribution size matches layer output, Nx12x32x32
      >>> attribution = layer_int_inf.attribute(input)
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code: def attribute(
      self,
      inputs: Union[Tensor, Tuple[Tensor, ...]],
      baselines: BaselineType = None,
      target: TargetType = None,
      additional_forward_args: Any = None,
      n_steps: int = 50,
      method: str = "gausslegendre",
      internal_batch_size: Union[None, int] = None,
      attribute_to_layer_input: bool = False,
  ) -> Union[Tensor, Tuple[Tensor, ...]]:
      r"""
      Args:
  
          inputs (Tensor or tuple[Tensor, ...]): Input for which internal
                      influence is computed. If forward_func takes a single
                      tensor as input, a single input tensor should be provided.
                      If forward_func takes multiple tensors as input, a tuple
                      of the input tensors should be provided. It is assumed
                      that for all given input tensors, dimension 0 corresponds
                      to the number of examples, and if multiple input tensors
                      are provided, the examples must be aligned appropriately.
          baselines (scalar, Tensor, tuple of scalar, or Tensor, optional):
                      Baselines define a starting point from which integral
                      is computed and can be provided as:
  
                      - a single tensor, if inputs is a single tensor, with
                        exactly the same dimensions as inputs or the first
                        dimension is one and the remaining dimensions match
                        with inputs.
  
                      - a single scalar, if inputs is a single tensor, which will
                        be broadcasted for each input value in input tensor.
  
                      - a tuple of tensors or scalars, the baseline corresponding
                        to each tensor in the inputs' tuple can be:
  
                        - either a tensor with matching dimensions to
                          corresponding tensor in the inputs' tuple
                          or the first dimension is one and the remaining
                          dimensions match with the corresponding
                          input tensor.
  
                        - or a scalar, corresponding to a tensor in the
                          inputs' tuple. This scalar value is broadcasted
                          for corresponding input tensor.
  
                      In the cases when `baselines` is not provided, we internally
                      use zero scalar corresponding to each input tensor.
  
                      Default: None
          target (int, tuple, Tensor, or list, optional): Output indices for
                      which gradients are computed (for classification cases,
                      this is usually the target class).
                      If the network returns a scalar value per example,
                      no target index is necessary.
                      For general 2D outputs, targets can be either:
  
                      - a single integer or a tensor containing a single
                        integer, which is applied to all input examples
  
                      - a list of integers or a 1D tensor, with length matching
                        the number of examples in inputs (dim 0). Each integer
                        is applied as the target for the corresponding example.
  
                      For outputs with > 2 dimensions, targets can be either:
  
                      - A single tuple, which contains #output_dims - 1
                        elements. This target index is applied to all examples.
  
                      - A list of tuples with length equal to the number of
                        examples in inputs (dim 0), and each tuple containing
                        #output_dims - 1 elements. Each tuple is applied as the
                        target for the corresponding example.
  
                      Default: None
          additional_forward_args (Any, optional): If the forward function
                      requires additional arguments other than the inputs for
                      which attributions should not be computed, this argument
                      can be provided. It must be either a single additional
                      argument of a Tensor or arbitrary (non-tuple) type or a
                      tuple containing multiple additional arguments including
                      tensors or any arbitrary python types. These arguments
                      are provided to forward_func in order following the
                      arguments in inputs.
                      For a tensor, the first dimension of the tensor must
                      correspond to the number of examples. It will be
                      repeated for each of `n_steps` along the integrated
                      path. For all other types, the given argument is used
                      for all forward evaluations.
                      Note that attributions are not computed with respect
                      to these arguments.
                      Default: None
          n_steps (int, optional): The number of steps used by the approximation
                      method. Default: 50.
          method (str, optional): Method for approximating the integral,
                      one of `riemann_right`, `riemann_left`, `riemann_middle`,
                      `riemann_trapezoid` or `gausslegendre`.
                      Default: `gausslegendre` if no method is provided.
          internal_batch_size (int, optional): Divides total #steps * #examples
                      data points into chunks of size at most internal_batch_size,
                      which are computed (forward / backward passes)
                      sequentially. internal_batch_size must be at least equal to
                      #examples.
                      For DataParallel models, each batch is split among the
                      available devices, so evaluations on each available
                      device contain internal_batch_size / num_devices examples.
                      If internal_batch_size is None, then all evaluations
                      are processed in one batch.
                      Default: None
          attribute_to_layer_input (bool, optional): Indicates whether to
                      compute the attribution with respect to the layer input
                      or output. If `attribute_to_layer_input` is set to True
                      then the attributions will be computed with respect to
                      layer inputs, otherwise it will be computed with respect
                      to layer outputs.
                      Note that currently it is assumed that either the input
                      or the output of internal layer, depending on whether we
                      attribute to the input or output, is a single tensor.
                      Support for multiple tensors will be added later.
                      Default: False
  
      Returns:
          *Tensor* or *tuple[Tensor, ...]* of **attributions**:
            - **attributions** (*Tensor* or *tuple[Tensor, ...]*):
                      Internal influence of each neuron in given
                      layer output. Attributions will always be the same size
                      as the output or input of the given layer depending on
                      whether `attribute_to_layer_input` is set to `False` or
                      `True` respectively.
                      Attributions are returned in a tuple if
                      the layer inputs / outputs contain multiple tensors,
                      otherwise a single tensor is returned.
  
      Examples::
  
          >>> # ImageClassifier takes a single input tensor of images Nx3x32x32,
          >>> # and returns an Nx10 tensor of class probabilities.
          >>> # It contains an attribute conv1, which is an instance of nn.conv2d,
          >>> # and the output of this layer has dimensions Nx12x32x32.
          >>> net = ImageClassifier()
          >>> layer_int_inf = InternalInfluence(net, net.conv1)
          >>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
          >>> # Computes layer internal influence.
          >>> # attribution size matches layer output, Nx12x32x32
          >>> attribution = layer_int_inf.attribute(input)
      """
      inputs, baselines = _format_input_baseline(inputs, baselines)
      _validate_input(inputs, baselines, n_steps, method)
      if internal_batch_size is not None:
          num_examples = inputs[0].shape[0]
          attrs = _batch_attribution(
              self,
              num_examples,
              internal_batch_size,
              n_steps,
              inputs=inputs,
              baselines=baselines,
              target=target,
              additional_forward_args=additional_forward_args,
              method=method,
              attribute_to_layer_input=attribute_to_layer_input,
          )
      else:
          attrs = self._attribute(
              inputs=inputs,
              baselines=baselines,
              target=target,
              additional_forward_args=additional_forward_args,
              n_steps=n_steps,
              method=method,
              attribute_to_layer_input=attribute_to_layer_input,
          )
  
      return attrs
  
file_path: captum/captum/attr/_core/layer/internal_influence.py
imports: from typing import Any, Callable, List, Tuple, Union
  import torch
  from captum._utils.common import _expand_additional_forward_args, _expand_target, _format_additional_forward_args, _format_output
  from captum._utils.gradient import compute_layer_gradients_and_eval
  from captum._utils.typing import BaselineType, TargetType
  from captum.attr._utils.approximation_methods import approximation_parameters
  from captum.attr._utils.attribution import GradientAttribution, LayerAttribution
  from captum.attr._utils.batching import _batch_attribution
  from captum.attr._utils.common import _format_input_baseline, _reshape_and_sum, _validate_input
  from captum.log import log_usage
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 2 for calling the InternalInfluence obj's underlying member method code with example usage: obj.attribute(inputs, baselines, target, additional_forward_args, n_steps, method, internal_batch_size, attribute_to_layer_input) and returns: attrs